IRJET- Criminal Identification by using Real Time Image Processing (original) (raw)
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Real time face recognition of video surveillance system using haar cascade classifier
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Real-Time Face Detection Security System using Haar Classifier Method
International Journal of Trend in Scientific Research and Development, 2018
Face Detection is concerned with finding whether or not there are any faces in a given images. Security and surveillance are the two important aspects of human being. Face detection is very important because it is not being safe in human environment. So, F Detection Security System is essential between individual in life. In the modern world everything is changed to provide a better life. So we were decided to develop the Real Time Face detection system. The importance of the face detection as it is esse surveillance and real user interfaces security to the country. Face differ in skin colour, nose, eyebrows, chin between different people in humanity. In this paper we effort is to develop system about face detection security system in the real time.
Use Of Haar Cascade Classifier For Face Tracking System In Real Time Video
International journal of engineering research and technology, 2013
In this paper, we present information about the face detection and tracking system with real time video as an input. The working method of this system is entirely divided into two main modules. The face recognition and detection from the video is the first module while the tracking is the second module. To detect the face in the image, Face Name Graph Matching algorithm is used. This algorithm involves various methods such as Haar-Cascade method, Open-cv libraries etc. Face clustering algorithm is used for tracking the face in the video. This system is mainly designed for the security purposes. Video recorded in public areas for known person or suspicious activities are used as an input for the system. System will identify and track one or more people captured in that video, this is taken as an output of this system. This paper involves working of face tracking system, algorithms which are involve and results of the system. KEYWORDSFace detection, Face recognition, Face tracking, Fa...
ARTIFICIALLY INTELLIGENT FACE DETECTION AND INVESTIGATION SYSTEM BASED ON OPENCV-FINAL-THESIS-2019
ARTIFICIALLY INTELLIGENT FACE DETECTION AND INVESTIGATION SYSTEM BASED ON OPENCV-FINAL-THESIS-2019, 2019
This research project aimed at designing and developing an artificially intelligent forensic face detection and identification system. The researchers used the Design science approach as the methodology. System requirements were collected by using interviews and document reviews for the artificially intelligent forensic face identification system. Data was analyzed using Microsoft word 2010. The findings from data analysis were used to design the core functionalities of the system. For system design, the researchers used Microsoft Visio 2007 to model architectural designs and data flow diagrams that were used to develop the system. Implementation of the project the researchers used the following technologies: Python, OpenCV, and IDL As a result, the researchers developed a system to help combat the abnormal increase in crime rates and a number of criminals has caused a great impact of insecurity in the nation. Crime prevention and criminal identification are the primary issue-challenges before security organizations because property and life protection are basic concerns. Physical human security interventions are limited, hence the advent of security technology specifically cameras especially CCTV that have been installed in many public and private areas to ensure surveillance. Footage from the CCTV can be used to detect, Recognize &identify wanted criminals on scene. In this paper, an automated facial recognition system with a criminal database was proposed using known Haar feature-based cascade classifier. This system will be able to detect and recognize faces in real-time. Accurate identification of faces is still a challenging task though the Viola-Jones framework has been widely used by researchers in order to detect the faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV, Tensor-Flow. Keywords: Criminal Identification; CCTV; facial recognition; Haar classifier; real-time; Viola-Jones; OpenCV.
IRJET- Facial Features Extraction and Recognition for Criminal Identification
IRJET, 2020
This paper represents a real time recognition and identification using an automatic surveillance camera and respective hardware. The proposed system involves 4 steps, including (1) training of real time data and pictures (2) face detection using Haar-Cascade classifier (3) comparison and matching of trained images with live images from camera (4) identification based on the comparison. A core application of interest is automated surveillance, where the aim is to acknowledge people from watch list. The purpose of this paper is to match an image with several already trained. This system represents a technique for face detection precisely in real time environment. Haar cascade is one of the prominent open source platforms for face detection. Here system uses Haar classifiers to trace faces in image using OpenCV platform. The accuracy of the face recognition is significantly high. The system can proficiently recognize one unique face, useful for quick search of suspected persons because the computation time is remarkably low. In India, we have a typical system for unique citizen recognition called Aadhaar. If system makes use of this as a citizenship database, it can differentiate between individuals and keep a record of the criminals in a specific region and add their identities to the criminal system watchlist.
Detection of Faces from Images Using Haar Cascade Classifier
Iconic Research and Engineering Journals, 2020
Nowadays, the increasing volume of images is absolutely demanded in most of digital image processing and pattern recognition. Moreover, face detection from images has become essential as it can be applied in various areas such as surveillance system, biometrics, gender classification, and so on. In this paper, Haar Cascade Classifier of Open Source Computer Vision Library (OpenCV) is utilized in detection of faces. In addition, Apache Hadoop, a distributed processing platform is applied to solve the computation time burden of face detection from large-scale images. According to the experimentation, the face detection with haar cascade classifier which is experimented on Apache Hadoop platform can offer satisfactory execution time results.
A Face Detection using Haar Like Feature Algorithm
Nowadays , the security forms the mostimportant section of our lives. Security of the home or the nearones is important to everybody. Home automation is an exciting area for security applications. Security cameras are utilized in order to build safety homes, and cities. However, this technology needs a person who detects any problem in the frame taken from the camera. In this paper, Haar Cascades is joined with python in order to detect the faces of people. For this purpose, to execute this system, a camera is useful So it helps to monitor and get notifications when motion is detected, captures the image and detect the faces, then sends images to a Smartphone via utilizing e mail used to see the activity and get notices when movement is detected.
Automatic Face Recognition and Detection Using OpenCV, Haar Cascade and Recognizer for Frontal Face
This research is based on real-time automatic frontal face recognition and detection using OpenCV, Haar Cascade and recognizers. The recognizers used are Eigenface, Fisherface and LBPH with Haar cascade. These algorithms are firstly trained with images stored in database and then the testing is done using real-time images captured through camera. The results have been compared based on accuracy of recognition rate. It is found that if we increase the distance between person and camera the Eigenface cannot detect and recognize the person properly. On other hand the LBPH and Fisherface gave best work performance withcapability to detect and recognize the authorized person with ±5% tilt angle and varyingfacial expressionsin both normal light condition (day) and low light conditions (night).
Real Time Face Detection and Recognition Using Haar Cascade and Support Vector Machines
COGS 109 Final Project c 2 o u y a n g @ u c s d. e d u Abstract — Face detection and recognition have both been active research areas over the past few decades and have been proven effective in many applications such as computer security and artificial intelligence. This paper introduces a practical system for tracking and recognizing faces in real time using a webcam. The first part of the system is facial detection, which is achieved using Haar featurebased cascade classifiers, a novel way proposed by Paul Viola and Michael Jones in their 2001 paper, " Rapid Object Detection using a Boosted Cascade of Simple Features " [1]. To further improve the method, geometric transformations are applied to each frame for face detection, allowing detection up to 45 degrees of head tilting. The second part of the system, face recognition, is achieved through a hybrid model consisting of feature extraction and classification trained on the cropped Extended Yale Face Database B [2]. To build the model, 2452 samples from 38 people in the database are splitted into training and testing sets by a ratio of 3:1. The top 150 eigenfaces are extracted from 1839 training faces in the database using Principal Component Analysis (PCA). The principal components are then feeded into the CSVM Classification model and trained with various kernel tricks. At the end of the recognition task, an accuracy of 93.3% is obtained with the Radial Basis Function (RBF) kernel on the testing set of 613 samples. Used in the real time application via webcam, the proposed system runs at 10 frames per second with high recognition accuracy relative to the number of training images of real time testers and how representative those training images are.
2020
This research is based on real-time automatic face recognition and detection which is performed at different angles with different light conditions using OpenCV library and recognizers. The recognizers used are Eigenface, Fisherface and LBPH with Haar cascade. These algorithms are first trained with images stored in database and then the testing is done using real-time images captured through camera. The results have been compared based on accuracy of recognition rate. It is found that with tilt frontal face the system using LBPH and Fisherface gave best work performance with ±10% tilt angle, and for complete up and down frontal face positions, the performance of system using Eigenface algorithm was better in both normal light condition (day) and low light condition (night). The overall performance comparison shows that LBPH has better results. KEYWORDS-Face Recognition and Detection, OpenCV, Haar Cascade, Eigenface Algorithm, Fisherface Algorithm, LBPH (Local Binary Pattern Histogr...